Multilingual signature-verification by generalized combined segmentation verification

Wataru Oyama, Yuuki Ogi, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


We propose a combined segmentation-verification technique for Multilingual signature verification. One limitation of original segmentation verification method is that it is not applicable for such signatures which are difficult in segmentation like Latin-scripts signatures. To overcome this limitation, we employ signature segmentation using the position of gravity center of whole signature strokes instead of an interval space between names in the signature image. Three grayscale-gradient features are extracted from whole signature image and two segmented signature images, left-hand and right-hand side and evaluated the Mahalanobis distances from reference samples. The on-line feature based technique employs dynamic programming (DP) matching for time series data of the two segmented and one whole signatures. Three resultant distance values from off-line verification and three resultant dissimilarity values are input to SVM to make final decision of genuine or forgery. We evaluated the performance of the proposed method on SigComp2011 dataset which consists of Chinese and Dutch signatures. In the results of evaluation, the proposed technique achieved 1.02% and 4.29% EER(Equal Error Rate)for Chinese and Dutch signatures respectively, which are significantly lower than and comparable to those of the best performances in SigComp2011 competition. These results confirm that the proposed generalized combined segmentation-verification by gravity center is effective for accuracy improvement of multi-script signature verification.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479918058
Publication statusPublished - Nov 20 2015
Externally publishedYes
Event13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, France
Duration: Aug 23 2015Aug 26 2015


Other13th International Conference on Document Analysis and Recognition, ICDAR 2015

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition


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